---
license: apache-2.0
base_model: motheecreator/vit-Facial-Expression-Recognition
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: vit-Facial-Expression-Recognition
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: image_folder
      type: image_folder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7444126074498567
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vit-Facial-Expression-Recognition

This model is a fine-tuned version of [motheecreator/vit-Facial-Expression-Recognition](https://huggingface.co/motheecreator/vit-Facial-Expression-Recognition) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7038
- Accuracy: 0.7444

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 0.7175        | 1.0   | 654  | 0.7309   | 0.7081          |
| 0.6952        | 2.0   | 1308 | 0.7379   | 0.6931          |
| 0.5041        | 3.0   | 1962 | 0.7444   | 0.7038          |
| 0.2461        | 4.0   | 2617 | 0.7393   | 0.7843          |
| 0.1846        | 5.0   | 3270 | 0.7391   | 0.8219          |
| 0.276         | 6.0   | 3924 | 0.8876   | 0.7335          |
| 0.2217        | 7.0   | 4578 | 0.9752   | 0.7255          |
| 0.0646        | 8.0   | 5232 | 1.0957   | 0.7263          |
| 0.063         | 9.0   | 5887 | 1.1335   | 0.7263          |
| 0.0562        | 10.0  | 6540 | 1.1663   | 0.7307          |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0